⭐ Add example code for Stable Diffusion(1.5) (#409)

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py

@@ -29,6 +29,41 @@ class StableDiffusion_1(LatentDiffusionModel):
         unet: The U-Net model.
         clip_text_encoder: The text encoder.
         lda: The image autoencoder.
+
+    Example:
+    ```py
+    import torch
+
+    from refiners.fluxion.utils import manual_seed, no_grad
+    from refiners.foundationals.latent_diffusion.stable_diffusion_1 import StableDiffusion_1
+
+    # Load SD
+    sd15 = StableDiffusion_1(device="cuda", dtype=torch.float16)
+
+    sd15.clip_text_encoder.load_from_safetensors("sd1_5.text_encoder.safetensors")
+    sd15.unet.load_from_safetensors("sd1_5.unet.safetensors")
+    sd15.lda.load_from_safetensors("sd1_5.autoencoder.safetensors")
+
+    # Hyperparameters
+    prompt = "a cute cat, best quality, high quality"
+    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+    seed = 42
+
+    sd15.set_inference_steps(50)
+
+    with no_grad():  # Disable gradient calculation for memory-efficient inference
+        clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
+        manual_seed(seed)
+
+        x = sd15.init_latents((512, 512)).to(sd15.device, sd15.dtype)
+
+        # Diffusion process
+        for step in sd15.steps:
+            x = sd15(x, step=step, clip_text_embedding=clip_text_embedding)
+
+        predicted_image = sd15.lda.decode_latents(x)
+        predicted_image.save("output.png")
+    ```
     """
 
     unet: SD1UNet